Çok boyutlu sırt çantası problemi için adaptif ikili yapay arı kolonisi algoritması (AİYAK)

Durgut, Rafet; Aydın, Mehmet Emin

doi:10.17341/gazimmfd.804858

Cited by 2 publications

(1 citation statement)

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“…Memetic algorithms [35], modified swarm intelligence algorithms [36], hybrid solutions [37] are the studies seeking for resolutions for this issue. Recently, adaptive operator selection schemes [8,38] look promising to overcome this issue in an easier way. The proposed approach in this study is based on a multi-strategy search approach, CSA-MSS, which implies adaptive use of multiple search strategies in order to overcome the limitations of single strategy search.…”

Section: Csa With Multi-strategy Search (Csa-mss)mentioning

confidence: 99%

Multi Strategy Search with Crow Search Algorithm

Durgut¹,

Aydın²

2022

Optimisation Algorithms and Swarm Intelligence

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Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.

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